/*
 *    SEAGenerator.java
 *    Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
 *    @author Albert Bifet
 *
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */
package moa.streams.generators;

import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;

import java.util.Random;

import moa.core.InstancesHeader;
import moa.core.ObjectRepository;
import moa.options.AbstractOptionHandler;
import moa.options.FlagOption;
import moa.options.IntOption;
import moa.streams.InstanceStream;
import moa.tasks.TaskMonitor;

// Generator described in paper:
//  W. Nick Street and YongSeog Kim
//    "A streaming ensemble algorithm (SEA) for large-scale classification",
//     KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
//     377-382 2001.

// Notes:
// The built in functions are based on the paper

public class SEAGenerator extends AbstractOptionHandler implements
		InstanceStream {

	@Override
	public String getPurposeString() {
		return "Generates SEA concepts functions.";
	}

	private static final long serialVersionUID = 1L;

	public IntOption functionOption = new IntOption("function", 'f',
			"Classification function used, as defined in the original paper.",
			1, 1, 4);

	public IntOption instanceRandomSeedOption = new IntOption(
			"instanceRandomSeed", 'i',
			"Seed for random generation of instances.", 1);

	public FlagOption balanceClassesOption = new FlagOption("balanceClasses",
			'b', "Balance the number of instances of each class.");

	public IntOption numInstancesConcept = new IntOption("numInstancesConcept", 'n',
			"The number of instances for each concept.", 0, 0, Integer.MAX_VALUE);

	public IntOption noisePercentageOption = new IntOption("noisePercentage",
			'n', "Percentage of noise to add to the data.", 10, 0, 100);

	protected interface ClassFunction {
		public int determineClass(double attrib1, double attrib2, double attrib3);
	}

	protected static ClassFunction[] classificationFunctions = {
			// function 1
			new ClassFunction() {
				public int determineClass(double attrib1, double attrib2, double attrib3) {
					return ( attrib1+attrib2 <= 8 ) ? 0 : 1;
				}
			},
			// function 2
			new ClassFunction() {
				public int determineClass(double attrib1, double attrib2, double attrib3) {
					return ( attrib1+attrib2 <= 9 ) ? 0 : 1;
				}
			},
			// function 3
			new ClassFunction() {
				public int determineClass(double attrib1, double attrib2, double attrib3) {
					return ( attrib1+attrib2 <= 7 ) ? 0 : 1;
				}
			},
			// function 4
			new ClassFunction() {
				public int determineClass(double attrib1, double attrib2, double attrib3) {
					return ( attrib1+attrib2 <= 9.5 ) ? 0 : 1;
				}
			}
	};

	protected InstancesHeader streamHeader;

	protected Random instanceRandom;

	protected boolean nextClassShouldBeZero;

	@Override
	protected void prepareForUseImpl(TaskMonitor monitor,
			ObjectRepository repository) {
		// generate header
		FastVector attributes = new FastVector();
		attributes.addElement(new Attribute("attrib1"));
		attributes.addElement(new Attribute("attrib2"));
		attributes.addElement(new Attribute("attrib3"));

		FastVector classLabels = new FastVector();
		classLabels.addElement("groupA");
		classLabels.addElement("groupB");
		attributes.addElement(new Attribute("class", classLabels));
		this.streamHeader = new InstancesHeader(new Instances(
				getCLICreationString(InstanceStream.class), attributes, 0));
		this.streamHeader.setClassIndex(this.streamHeader.numAttributes() - 1);
		restart();
	}

	public long estimatedRemainingInstances() {
		return -1;
	}

	public InstancesHeader getHeader() {
		return this.streamHeader;
	}

	public boolean hasMoreInstances() {
		return true;
	}

	public boolean isRestartable() {
		return true;
	}

	public Instance nextInstance() {
		double attrib1 = 0, attrib2  = 0, attrib3  = 0;
		int group = 0;
		boolean desiredClassFound = false;
		while (!desiredClassFound) {
			// generate attributes
			attrib1 = 10 * this.instanceRandom.nextDouble();
			attrib2 = 10 * this.instanceRandom.nextDouble();
			attrib3 = 10 * this.instanceRandom.nextDouble();

			// determine class
			group = classificationFunctions[this.functionOption.getValue() - 1]
					.determineClass(attrib1, attrib2, attrib3);
			if (!this.balanceClassesOption.isSet()) {
				desiredClassFound = true;
			} else {
				// balance the classes
				if ((this.nextClassShouldBeZero && (group == 0))
						|| (!this.nextClassShouldBeZero && (group == 1))) {
					desiredClassFound = true;
					this.nextClassShouldBeZero = !this.nextClassShouldBeZero;
				} // else keep searching
			}
		}
		//Add Noise
		if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption
					.getValue()) {
				group= (group == 0 ? 1 : 0);
			}

		// construct instance
		InstancesHeader header = getHeader();
		Instance inst = new DenseInstance(header.numAttributes());
		inst.setValue(0, attrib1);
		inst.setValue(1, attrib2);
		inst.setValue(2, attrib3);
		inst.setDataset(header);
		inst.setClassValue(group);
		return inst;
	}

	public void restart() {
		this.instanceRandom = new Random(this.instanceRandomSeedOption
				.getValue());
		this.nextClassShouldBeZero = false;
	}

	public void getDescription(StringBuilder sb, int indent) {
		// TODO Auto-generated method stub

	}

}
